Clues to quality of journals
Open Access Policy of Helmholtz Association 2016
CC Licencences
Publication Metrics
Home
Deutsch
Advanced Search
Browse by ...
Publication overview
Statistics (last 5 years)
OA Publications
Submit Publication
Import publication from...
Report missing publication
Contact persons
Help
Text
Endnote (RIS)
BibTeX
Publ. Version/Full Text
DOI
PMC
 |
Closed |
|
Open Access Green as soon as Postprint is submitted to ZB.
DataSHIELD: Taking the analysis to the data, not the data to the analysis.
Int. J. Epidemiol. 43, 1929-1944 (2014)
BACKGROUND: Research in modern biomedicine and social science requires sample sizes so large that they can often only be achieved through a pooled co-analysis of data from several studies. But the pooling of information from individuals in a central database that may be queried by researchers raises important ethico-legal questions and can be controversial. In the UK this has been highlighted by recent debate and controversy relating to the UK's proposed 'care.data' initiative, and these issues reflect important societal and professional concerns about privacy, confidentiality and intellectual property. DataSHIELD provides a novel technological solution that can circumvent some of the most basic challenges in facilitating the access of researchers and other healthcare professionals to individual-level data. METHODS: Commands are sent from a central analysis computer (AC) to several data computers (DCs) storing the data to be co-analysed. The data sets are analysed simultaneously but in parallel. The separate parallelized analyses are linked by non-disclosive summary statistics and commands transmitted back and forth between the DCs and the AC. This paper describes the technical implementation of DataSHIELD using a modified R statistical environment linked to an Opal database deployed behind the computer firewall of each DC. Analysis is controlled through a standard R environment at the AC. RESULTS: Based on this Opal/R implementation, DataSHIELD is currently used by the Healthy Obese Project and the Environmental Core Project (BioSHaRE-EU) for the federated analysis of 10 data sets across eight European countries, and this illustrates the opportunities and challenges presented by the DataSHIELD approach. CONCLUSIONS: DataSHIELD facilitates important research in settings where: (i) a co-analysis of individual-level data from several studies is scientifically necessary but governance restrictions prohibit the release or sharing of some of the required data, and/or render data access unacceptably slow; (ii) a research group (e.g. in a developing nation) is particularly vulnerable to loss of intellectual property-the researchers want to fully share the information held in their data with national and international collaborators, but do not wish to hand over the physical data themselves; and (iii) a data set is to be included in an individual-level co-analysis but the physical size of the data precludes direct transfer to a new site for analysis.
Impact Factor
Scopus SNIP
Web of Science
Times Cited
Times Cited
Scopus
Cited By
Cited By
Altmetric
9.197
2.719
25
128
Annotations
Special Publikation
Hide on homepage
Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Datashield ; Elsi ; Bioinformatics ; Confidentiality ; Disclosure ; Distributed Computing ; Intellectual Property ; Pooled Analysis ; Privacy
Language
english
Publication Year
2014
HGF-reported in Year
2015
ISSN (print) / ISBN
0300-5771
e-ISSN
1464-3685
Quellenangaben
Volume: 43,
Issue: 6,
Pages: 1929-1944
Publisher
Oxford University Press
Reviewing status
Peer reviewed
Institute(s)
Institute of Epidemiology (EPI)
POF-Topic(s)
30202 - Environmental Health
Research field(s)
Genetics and Epidemiology
PSP Element(s)
G-504091-001
G-504000-001
G-504000-001
PubMed ID
25261970
Scopus ID
84922363295
Erfassungsdatum
2015-02-10